ArticleChaos (Woodbury, N.Y.)2022
Fast algorithm to identify minimal patterns of synchrony through fibration symmetries in large directed networks.
Article in Chaos (Woodbury, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Symmetries in metabolic networks ofPNAS nexus · 2025Article
- Article
- Fibration symmetries and cluster synchronization in the Caenorhabditis elegans connectome.PloS one · 2024Article
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5 authors.
Funding
Abstract
Recent studies have revealed the interplay between the structure of network circuits with fibration symmetries and the functionality of biological networks within which they have been identified. The presence of these symmetries in complex networks predicts the phenomenon of cluster synchronization, which produces patterns of a synchronized group of nodes. Here, we present a fast, and memory efficient, algorithm to identify fibration symmetries in networks. The algorithm is particularly suitable for large networks since it has a runtime of complexity O(MlogN) and requires O(M+N) of memory resources, where N and M are the number of nodes and edges in the network, respectively. The algorithm is a modification of the so-called refinement paradigm to identify circuits that are symmetrical to information flow (i.e., fibers) by finding the coarsest refinement partition over the network. Finally, we show that the algorithm provides an optimal procedure for identifying fibers, overcoming current approaches used in the literature.
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